A Boosting Approach for Automated Trading

Germán Creamer and Yoav Freund

Trading Spring 2007, 2007 (1) 67-78

Germán Creamer

An adjunct associate research scientist at the Center for Computational Learning Systems, Columbia University in New York, NY and an affiliated professor at Centrum Católica, Pontificia Universidad Católica del Perú in Lima, Perú. ggc14@columbia.edu

Abstract

This article describes an algorithm for short-term technical trading. The algorithm was tested in the context of the Penn-Lehman Automated Trading (PLAT) competition. The algorithm is based on three main ideas. The first idea is to use a combination of technical indicators to predict the daily trend of the stock, the combination is optimized using a boosting algorithm. The second idea is to use the constant rebalanced portfolios within the day in order to take advantage of market volatility without increasing risk. The third idea is to use limit orders rather than market orders in order to minimize transaction costs.